Improving the Economics of Large-Scale AI

As AI scales across industries, networks are increasingly the bottleneck — delaying training, underutilizing accelerators, and degrading inference performance. Cornelis Networks’ CN5000 fabric directly addresses these issues with a purpose-built congestion management architecture optimized for the entire AI lifecycle: training, fine-tuning, and inference.

The white paper explores how CN5000 eliminates tail latency, accelerates synchronization, and delivers real-time performance with lower power and infrastructure overhead. With support for 400G networks and deep telemetry, CN5000 maximizes ROI by reducing operational costs and improving job completion times. As AI models grow larger and more latency-sensitive, traditional networking solutions can’t keep pace — leaving infrastructure constrained and business value unrealized. CN5000 delivers on all three pillars of modern AI infrastructure: performance, TCO, and scalability. Whether enabling collective communications for large-scale model training or ensuring deterministic responses in inference pipelines, CN5000 provides the agility and efficiency needed to stay competitive in the age of AI.